Characteriza*on  and  visualiza*on  of  compound  combina*on  responses  in  

a  high  throughout  se8ng  

Rajarshi  Guha,  Lesley  Mathews,  John  Keller,  Paul  Shinn,  Craig  Thomas,  Anton  

Simeonov,  Marc  Ferrar  

NIH-­‐NCATS    

April  7,  2013,  New  Orleans  

Outline  

hRp://origin.arstechnica.com/news.media/pills-­‐4.jpg  

Why  combine?  

Physical  infrastructure  &  workflow  

Summarizing  and  exploring  the  data  

Screening  for  Novel  Drug  Combina*ons  

•  Drug  combina*ons  offer  advantages  for  both  efficacy  and  poten*al  reduc*on  of  target  related  toxici*es  

•  Combina*on  studies  also  offer  insight  into  systems  level  interac*ons  

How  to  Test  Combina*ons  

•  Many  procedures  described  in  the  literature  – Fixed  dose  ra*o  (aka  ray)  – Ray  contour  – Checkerboard  – Gene*c  algorithm    

C5,D5 C5

C4,D4 C4

C3,D3 C3

C2,D2 C2

C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1

D5 D4 D3 D2 D1 0

Scaling  Response  Surface  Screening  

•  Response  surfaces    imply  a  DxD  matrix    for  each  combina*on  

•  All  pairs  screening  is    imprac*cal  for  more    than  tens  of      compounds  

•  Instead  we  consider  N  compounds  versus  a  fixed  size  library    

0e+00

1e+07

2e+07

3e+07

4e+07

5e+07

250 500 750 1000

Number of compoundsN

umbe

r of c

ombi

natio

ns

Combination type

All pairs

Fixed library

Dose matrix size

4

6

10

Development VEGF signaling and activation

Translation Non-genomic (rapid) action of Androgen Receptor

Transcription PPAR Pathway

Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR

Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling

Cell adhesion Chemokines and adhesion

Apoptosis and survival Anti-apoptotic action of Gastrin

Development VEGF signaling via VEGFR2 - generic cascades

Some pathways of EMT in cancer cells

Development EGFR signaling pathway

0 5 10 15-log10(pValue)

Mechanism  Interroga*on  PlateE  

0

50

100

150

200

Approved

Discontinued

Phase

I

Phase

II

Phase

III

Preclinical

R&D

Suppliment

Num

ber o

f com

poun

ds

kinasenucleic acid bindingreceptorsignaling moleculetransferase

Top 10 Panther gene classesTop  10  Panther  gene  classes  

Top  10  enriched  GeneGo  pathway  maps  

Combina*on  Screening  Workflow  Run  single  agent  dose  responses  

6x6  matrices  for    poten5al  synergies  

10x10  for  confirma5on  +  self-­‐cross  

Acoustic dispense, 15 min for 1260 wells, 14 min for

1200 wells"

Repor*ng  Combina*on  Results  

Repor*ng  Combina*on  Results  

Repor*ng  Combina*on  Results  

•  These  web  pages  and  matrix  layouts  are  a  useful  first  step  

•  Does  not  scale  as  we  grow  MIPE    •  S*ll  need  to  do  a  beRer  job  of  ranking  and  aggrega*ng  combina*on  responses  taking  into  account  – Response  matrix  – Compounds,  targets  and  pathways  

A  Simpler  Visual  Summary  

•  Convert  mul*ple  individual    heatmaps,  to  a  single  heatmap    by  unrolling  response  matrices  

•  Examine  effects  of  A  at  fixed  concentra*ons,  on  dose  response  of  B  

•  Zoom  in  on  combina*ons  that  show  extensive  ac*vity  throughout  the  dose  matrix  

1 7 13 19 25 31

2 8 14 20 26 32

3 9 15 21 27 33

4 10 16 22 28 34

5 11 17 23 29 35

6 12 18 24 30 36

{1, 2, 3, 4, …, 34, 35, 36}

A  Simpler  Visual  Summary  

Concentration Combination1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

When  are  Combina*ons  Similar?  

•  Differences  and  their  aggregates  such  as  RMSD  can  lead  to  degeneracy  

•  Instead  we’re  interested  in  the  shape  of  the  surface  

•  How  to  characterize  shape?  – Parametrized  fits  – Distribu*on  of  responses  

0.000

0.005

0.010

0 25 50 75 100

0.00

0.02

0.04

0.06

0 25 50 75 100

0.00

0.05

0.10

0.15

0 50 100

D, p value

0

3

6

9

0.00 0.25 0.50 0.75 1.00D

density

Similarity  via  the  KS  Test  

•  Quan*fy  distance  between  response  distribu*ons  via  KS  test  –  If  p-­‐value  >  0.05,  we  assume  distance  is  0  

•  But  ignores  the  spa5al  distribu*on  of  the  responses  on  the  concentra*on  grid  

0.0

2.5

5.0

7.5

10.0

0.00 0.25 0.50 0.75D

density

Similarity  via  the  Syrjala  Test  

•  Syrjala  test  used  to  compare  popula*on  distribu*ons  over  a  spa*al  grid  –  Invariant  to  grid  orienta*on  – Provides  an  empirical  p-­‐value  

•  Less  degenerate  than  just  considering  1D  distribu*ons  

Syrjala,  S.E.,  “A  Sta*s*cal  Test  for  a  Difference  between  the  Spa*al  Distribu*ons  of  Two  Popula*ons”,  Ecology,  1996,  77(1),  75-­‐80  

Datasets  

•  Primary  focus  is  on  inves*ga*ng  combina*ons  with  Ibru*nib  for  treatment  of  DLBCL  – Btk  inhibitor  –  In  Phase  II  trials  – Experiments  run  in  the  TMD8  cell  line,  tes*ng  for  cell  viability    

Clustering  Response  Surfaces  0.0

0.2

0.4

0.6

0.8

C1  (24)  

C2(47)  

C3(35)  

C4(24)  

response to stress

peptidyl-tyrosine phosphorylation

cell cycle checkpoint

interphase

peptidyl-amino acid modification

negative regulation of cell cycle

cellular process involved in reproduction

ubiquitin-dependent protein catabolic process

regulation of interferon-gamma-mediated signaling pathway

macromolecule catabolic process

0 1 2 3-log10(Pvalue)

Cluster  C3  

•  Vargatef,  vorinostat,  flavopiridol,  …  

•  Not  par*cularly  specific  given  the  range  of  primary  targets  

0.00

0.05

0.10

0.15

0.20

0.25

0.30

302

281

128

174

285

153

177

210

144 35 60 457

180 39 111

272

288

166

231

104

106

417

319 44 218

279

219

121

119 34 102

286

230

178

179

Cluster  C4  

•  Focus  on  sugar  metabolism    

•  Ruboxistaurin,  cycloheximide,  2-­‐methoxyestradiol,  …  

•  PI3K/Akt/mTOR  signalling  pathways  glycogen metabolic process

regulation of glycogen biosynthetic process

glucan biosynthetic process

glucan metabolic process

cellular polysaccharide metabolic process

regulation of generation of precursor metabolites and energy

peptidyl-serine phosphorylation

cellular macromolecule localization

regulation of polysaccharide biosynthetic process

cellular carbohydrate biosynthetic process

0 1 2 3-log10(Pvalue)

0.00

0.02

0.04

0.06

0.08

361

254

215

164

143 82 125

327

241

194

145

116

139

371

163

165

384

339

322

217

184

150 52 136

Combina*ons  across  Cell  Lines  

•  Cellular  background  affects  responses  •  Can  we  group  cell  lines  based  on  combina*on  response?  

Working  in  Combina*on  Space  

•  Each  cell  line  is  represented  as  a  vector  of  response  matrices  

•  “Distance”  between  two    cell  lines  is  a  func*on  of  the  distance  between  component  response  matrices      

•  F  can  be  min,  max,  mean,  …    

L1   L2  

=  d1  

=  d2  

=  d3  

=  d4  

=  d5  

D L1,L2( ) = F({d1,d2,…,dn})

,  

,  

,  ,  ,  

Many  Choices  to  Make  0

12

34

KMS-34

INA-6

L363

OPM-1

XG-2

FR4

AMO-1

XG-6

MOLP-8

ANBL-6

KMS-20

XG-7

OCI-MY1

XG-1

8226

EJM

U266

KMS-11LB

SKMM-1

MM-MM1

sum

0.0

0.1

0.2

0.3

0.4

0.5

0.6

L363

OPM-1

XG-2

KMS-20

XG-1

XG-7

ANBL-6

OCI-MY1

U266

XG-6

INA-6

MOLP-8

AMO-1

KMS-34

KMS-11LB

SKMM-1

MM-MM1

EJM FR4

8226

max

0.00

0.05

0.10

0.15

0.20

0.25

INA-6

MM-MM1

8226

XG-1

U266

ANBL-6

SKMM-1

EJM

OPM-1

XG-2

OCI-MY1

KMS-20

L363

KMS-11LB

AMO-1

XG-6

FR4

KMS-34

MOLP-8

XG-7

min

0.0

0.2

0.4

0.6

0.8

1.0

1.2

L363

OPM-1

XG-2

KMS-34

INA-6

KMS-11LB

SKMM-1

EJM

U266

MM-MM1

FR4

AMO-1

XG-6

8226

MOLP-8

ANBL-6

OCI-MY1

XG-1

KMS-20

XG-7

euc

•  Vargatef  exhibited  anomalous  matrix  response  compared  to  other  VEGFR  inhibitors            

Exploi*ng  Polypharmacology  

Vargatef  

Linifanib Axitinib Sorafenib Vatalanib

Motesanib Tivozanib Brivanib Telatinib

Cabozantinib Cediranib BMS-794833 Lenvatinib

OSI-632 Foretinib Regorafenib

Exploi*ng  Polypharmacology  •  PD-­‐166285  is  a  SRC  &  FGFR  inhibitor  

•  Lestaurnib  has    ac*vity  against  FLT3  

Vargatef DCC-2036 PD-166285 GDC-0941

PI-103 GDC-0980 Bardoxolone methyl AT-7519AT7519

SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024

ISOX Belinostat PF-477736 AZD-7762

Chk1 IC50 = 105 nM

VEGFR-1

VEGFR-2

VEGFR-3

FGFR-1

FGFR-2

FGFR-3

FGFR-4

PDGFRa

PDGFRb

Flt-3

Lck

Lyn

Src

0 200 400 600Potency (nM)

Hilberg,  F.  et  al,  Cancer  Res.,  2008,  68,  4774-­‐4782  

Predic*ng  Synergies  

•  Related  to  response  surface  methodologies  •  LiRle  work  on  predic*ng  drug  response  surfaces  – Peng  et  al,  PLoS  One,  2011  –  Jin  et  al,  Bioinforma5cs,  2011  – Boik  &  Newman,  BMC  Pharmacology,  2008  – Lehar  et  al,  Mol  Syst  Bio,  2007  

•  But  synergy  is  not  always  objec*ve  and  doesn’t  really  correlate  with  structure  

Structural  Similarity  vs  Synergy  beta gamma

ssnum Win 3x3

0.1

0.2

0.3

0.4

0.1

0.2

0.3

0.4

0.1

0.2

0.3

0.4

0.1

0.2

0.3

0.4

0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05

0 5 10 15 20 25 -40 -30 -20 -10 0Synergy measure

Similarity

Predic*on  Strategy  

•  Don’t  directly  predict  synergy  •  Use  single  agent  data  to  generate  a  model  surface  

•  Predict  combina*on  responses  •  Characterize  synergy  of  predicted  response  with  respect  to  model  surface      

•  Reduced  to  a  mixture  predic*on  problem  •  Will  likely  be  beRer  addressed  by  (also)  considering  target  connec*vity    

Conclusions  

•  Use  response  surfaces  as  first  class  descriptors  of  drug  combina*ons  –  Surrogate  for  underlying  target  network  connec*vity  (?)  

•  Response  surface  similarity  based  on  distribu*ons  is  (fundamentally)  non-­‐parametric  

•  Going  from  single  -­‐  chemical  space  to  combina*on  space  opens  up  interes*ng  possibili*es  

•  Manual  inspec*on  is  s*ll  a  vital  step  

Acknowledgements  

•  Lou  Staudt  •  Beverly  Mock,  John  Simmons